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Measuring the worldwide spread of COVID-19 using a comprehensive modeling method
BACKGROUND: With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504693/ https://www.ncbi.nlm.nih.gov/pubmed/37715170 http://dx.doi.org/10.1186/s12911-023-02213-4 |
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author | Zhou, Xiang Ma, Xudong Gao, Sifa Ma, Yingying Gao, Jianwei Jiang, Huizhen Zhu, Weiguo Hong, Na Long, Yun Su, Longxiang |
author_facet | Zhou, Xiang Ma, Xudong Gao, Sifa Ma, Yingying Gao, Jianwei Jiang, Huizhen Zhu, Weiguo Hong, Na Long, Yun Su, Longxiang |
author_sort | Zhou, Xiang |
collection | PubMed |
description | BACKGROUND: With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models. METHODS: We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model. RESULTS: All 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9–12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10–20% of the countries’ populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner. CONCLUSIONS: We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02213-4. |
format | Online Article Text |
id | pubmed-10504693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105046932023-09-17 Measuring the worldwide spread of COVID-19 using a comprehensive modeling method Zhou, Xiang Ma, Xudong Gao, Sifa Ma, Yingying Gao, Jianwei Jiang, Huizhen Zhu, Weiguo Hong, Na Long, Yun Su, Longxiang BMC Med Inform Decis Mak Research BACKGROUND: With the global spread of COVID-19, detecting high-risk countries/regions timely and dynamically is essential; therefore, we sought to develop automatic, quantitative and scalable analysis methods to observe and estimate COVID-19 spread worldwide and further generate reliable and timely decision-making support for public health management using a comprehensive modeling method based on multiple mathematical models. METHODS: We collected global COVID-19 epidemic data reported from January 23 to September 30, 2020, to observe and estimate its possible spread trends. Countries were divided into three outbreak levels: high, middle, and low. Trends analysis was performed by calculating the growth rate, and then country grouping was implemented using group-based trajectory modeling on the three levels. Individual countries from each group were also chosen to further disclose the outbreak situations using two predicting models: the logistic growth model and the SEIR model. RESULTS: All 187 observed countries' trajectory subgroups were identified using two grouping strategies: with and without population consideration. By measuring epidemic trends and predicting the epidemic size and peak of individual countries, our study found that the logistic growth model generally estimated a smaller epidemic size than the SEIR model. According to SEIR modeling, confirmed cases in each country would take an average of 9–12 months to reach the outbreak peak from the day the first case occurred. Additionally, the average number of cases at the peak time will reach approximately 10–20% of the countries’ populations, and the countries with high trends and a high predicted size must pay special attention and implement public health interventions in a timely manner. CONCLUSIONS: We demonstrated comprehensive observations and predictions of the COVID-19 outbreak in 187 countries using a comprehensive modeling method. The methods proposed in this study can measure COVID-19 development from multiple perspectives and are generalizable to other epidemic diseases. Furthermore, the methods also provide reliable and timely decision-making support for public health management. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02213-4. BioMed Central 2023-09-15 /pmc/articles/PMC10504693/ /pubmed/37715170 http://dx.doi.org/10.1186/s12911-023-02213-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhou, Xiang Ma, Xudong Gao, Sifa Ma, Yingying Gao, Jianwei Jiang, Huizhen Zhu, Weiguo Hong, Na Long, Yun Su, Longxiang Measuring the worldwide spread of COVID-19 using a comprehensive modeling method |
title | Measuring the worldwide spread of COVID-19 using a comprehensive modeling method |
title_full | Measuring the worldwide spread of COVID-19 using a comprehensive modeling method |
title_fullStr | Measuring the worldwide spread of COVID-19 using a comprehensive modeling method |
title_full_unstemmed | Measuring the worldwide spread of COVID-19 using a comprehensive modeling method |
title_short | Measuring the worldwide spread of COVID-19 using a comprehensive modeling method |
title_sort | measuring the worldwide spread of covid-19 using a comprehensive modeling method |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10504693/ https://www.ncbi.nlm.nih.gov/pubmed/37715170 http://dx.doi.org/10.1186/s12911-023-02213-4 |
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